Ross Meentemeyer

Rapid responses and data-driven decision support tools are essential for understanding and mitigating threats posed by yield-damaging agricultural pests and pathogens. However, sparse data are well-known challenges limiting the accuracy and iterative improvement of pest spread models. This research will couple advances in image classification with vetted crowdsourced and satellite imagery to build an automated, repeatable pipeline for scaling host mapping efforts essential to forecasting pest spread. The resulting spatially-explicit maps of host species at scale will improve pest risk forecasting by addressing sparse data concerns and reducing data latency, thereby enabling iterative updating of the model parameters as new data become available and shortening time to decision making. We will specifically focus on fruit and tree nuts that represent an economically and culturally significant crop in the United States threatened by emerging pests and climate change. Throughout all aspects of the project, we will collaborate and build on our partnerships with USDA APHIS and ARS, state departments of agriculture, and growers associations to identify key threats to fruit and tree nut crops, iteratively validate host species maps and model forecasts, and co-develop a user-friendly decision support tool and alert system that translates forecasts and simulations into actionable insights. Our iterative near-term forecasting system coupled with data inputs created using machine learning will reduce costs for pest surveys and help growers identify when and where to intervene to protect their crops, thus reducing production losses and chemical inputs.������������������

Epidemic invasions have substantial impacts on both ecosystem function and human welfare (1,16,31,67,91), and may become more frequent owing to globalization (116). Understanding the establishment and spread of such diseases can contribute significantly to identifying appropriate disease control strategies (35,96,115). Pathogens demonstrating long-distance dispersal (LDD) are of particular concern, owing to their potential to rapidly spread over large spatial scales. This includes pathogens with propagules that have the potential for long-distance transport through air, such as foot-and-mouth disease (FMD) (60), West Nile Virus (90), avian influenza (62), white-nose syndrome of bats (4) and many diseases of plants (8), through water, such as Aspergillosis of coral (134), and perhaps also pathogens spread through human transport systems, such as influenza (66) and Ebola virus (40). Bird migration can result in fat-tailed, LDD dispersal patterns, with dispersal over hundreds or thousands of kilometers (95,130). Similarly, "anomalous diffusion" has been suggested to result in fat-tailed distributions and superdiffusive spread of a range of organisms (6,131). Developing effective models for such large-scale processes remains a challenge, and will likely require a range of approaches and comparative studies encompassing a diversity of pathogens and hosts.

The primary purpose of this agreement is to cooperatively develop a framework to allow for integration of geospatial information systems as well as geospatial products into a Geospatial Hub hosted within an existing platform at USDA. All constituent parts of USDA (mission areas, agencies, services) have geospatial investments and use. There is a need for a platform to 1. communicate the impact of geospatial programs and 2. allow for data sharing within USDA and between USDA, academia, private sector, other Federal agencies, and the public.

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world and in the US. Climate change is exacerbating weather events that affect crop production and food access for vulnerable areas. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover and evolution of new pathogen genetic lineages. Prediction of plant disease pandemics is unreliable due to the lack of real-time detection, surveillance and data analytics to inform decisions and prevent spread. In order to tackle these grand challenges, a new set of predictive tools are needed. In the PIPP Phase I project, our multidisciplinary team will develop a pandemic prediction system called ����������������Plant Aid Database (PAdb)��������������� that links pathogen transmission biology, disease detection by in-situ and remote sensing, genomics of emerging pathogen strains and real-time spatial and temporal data analytics and predictive simulations to prevent pandemics. We plan to validate the PAdb using several model pathogens including novel and host resistance breaking strains of lineages of two Phytophthora species, Phytophthora infestans and P. ramorum and the cucurbit downy mildew pathogen Pseudoperonspora cubensis Adoption of new technologies and mitigation interventions to stop pandemics require acceptance by society. In our work, we will also characterize how human attitudes and social behavior impact disease transmission and adoption of surveillance and sensor technologies by engaging a broad group of stakeholders including growers, extension specialist, the USDA APHIS, Department of Homeland Security and the National Plant Diagnostic Network in a Biosecurity Preparedness workshop. This convergence science team will develop tools that help mitigate future plant disease pandemics using predictive intelligence. The tools and data can help stakeholders prevent spread from initial source populations before pandemics occur and are broadly applicable to animal and human pandemic research.

The scope of work under this agreement will consist of three major functions: 1) GIS research, development and technical support for parks and programs of the Northeast Region of the NPS, 2) assistance with strategic and tactical planning for GIS implementation and 3) operational testing and deployment help with Enterprise GIS initiatives and designs. The Center for Geospatial Analytics at North Carolina State University has worked with the Northeast Region of the NPS for over 20 years in the development of GIS for park management. This activity has led to major advances in the planning and application of GIS technology in the NPS and has placed the Northeast Region among the leaders within the NPS in this regard.

The goal of this proposal is to build economic cost benefit analysis into spread model treatment scenarios. We will do this by adding an economic module and allow for multiple hosts with different values and abilities to increase the spread of the pest.

The primary purpose of this agreement is to develop algorithms and ensemble predictions that 1) fully quantify uncertainty in host map distributions, 2) are continuously updated as new data sources become available, 3) have full accuracy statistics, and 4) are fully open-source and able to be used and built on by other researchers and analysts. These algorithms will be tested on host species across 3 use types: annual crop, perennial crop, and forest host. By examining hosts across a wide range of crop and forest hosts we can ensure that the algorithms and ensembles are generalized enough to be used beyond the specified species examined during the project. This is the second year of the project.

The primary purpose of this agreement is to develop the eRADS (eradication analysis and decision support) tool and the algorithms and workflow to help evaluate the feasibility of entering an eradication or containment program following a new pest incursion and to suggest effective strategies for managing the new pest. Specifically focused on integrating spatial economic models into the framework.

Riparian buffers have been used to protect water quality from human land uses for decades, and their impacts at local and stream reach segments are well established. What is not well understood is the scale and placement of riparian buffers required to improve water quality across regional scale watersheds, thus protecting coastal ecosystem health from upstream development and agricultural land uses, particularly in the context of changing land use and climate. The outcome goals of the Healthy Coastal Ecosystems focal area of North Carolina Sea Grant Strategic Plan emphasize the critical need for wholistic, watershed approaches that include upstream-downstream connectivity and the impacts of changing climate and land use on watershed health. Our project goal is to apply a human-natural systems watershed approach address critical gaps in scientific understanding that forward the outcomes of the Healthy Coastal Ecosystems Focal area 1. Identify the role that upstream land use change plays on downstream (coastal) water quality in the context of changing climate 2. Examine the role that riparian buffer protection policy might play in mitigating the impacts of climate and land use change on downstream, coastal water quality. We will test a central hypothesis that strategic buffers, those placed on local watersheds with the greatest extents of either developed or row crop agriculture with provide significant improvements to whole watershed health (stream flow flashiness, sediment and nutrients) as measured across sub-watersheds and at the coastal watershed outlet. We will compare the watershed health benefits of strategic buffers only in the Piedmont, urbanizing region of the Cape Fear River basin to the placement of buffers throughout the entire basin. We will analyze the effectiveness of strategic buffers to business as usual (no mandated buffers) as well as complete buffers (all streams). To ensure results of our project reach stakeholders, we will form a Stakeholder Advisory Board that will provide regular assessments of project success.

The primary purpose of this agreement is to develop algorithms for determining optimal pest management solutions. We will use four algorithms: Approximate Bayesian Computation, Genetic Algorithms, Simulated Annealing, and Probabilistic Programming. Further, we expand on these approaches by building an ensemble of the optimal management solutions from each algorithm to provide a more robust approach than any one algorithm alone. Decision makers and stakeholders can view the effectiveness of each solution - measured by preventing quarantine escape, minimizing populations, or conserving agricultural commodities or tree species - on the Tangible Landscape, the PoPS dashboard, or any GIS platform.